Deinterlacing using modular neural network

Dong Hun Woo; Il Kyu Eom; Yoo Shin Kim

doi:10.1117/12.532554

28 May 2004 Deinterlacing using modular neural network

Dong Hun Woo, Il Kyu Eom, Yoo Shin Kim

Proceedings Volume 5298, Image Processing: Algorithms and Systems III; (2004) https://doi.org/10.1117/12.532554
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

Deinterlacing is the conversion process from the interlaced scan to progressive one. While many previous algorithms that are based on weighted-sum cause blurring in edge region, deinterlacing using neural network can reduce the blurring through recovering of high frequency component by learning process, and is found robust to noise. In proposed algorithm, input image is divided into edge and smooth region, and then, to each region, one neural network is assigned. Through this process, each neural network learns only patterns that are similar, therefore it makes learning more effective and estimation more accurate. But even within each region, there are various patterns such as long edge and texture in edge region. To solve this problem, modular neural network is proposed. In proposed modular neural network, two modules are combined in output node. One is for low frequency feature of local area of input image, and the other is for high frequency feature. With this structure, each modular neural network can learn different patterns with compensating for drawback of counterpart. Therefore it can adapt to various patterns within each region effectively. In simulation, the proposed algorithm shows better performance compared with conventional deinterlacing methods and single neural network method.

Citation Download Citation

Dong Hun Woo, Il Kyu Eom, and Yoo Shin Kim "Deinterlacing using modular neural network", Proc. SPIE 5298, Image Processing: Algorithms and Systems III, (28 May 2004); https://doi.org/10.1117/12.532554

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